Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model
Abstract Angio-based microvascular resistance (AMR) as a potential alternative to the index of microcirculatory resistance (IMR) and its relationship with microvascular obstruction (MVO) and other cardiac magnetic resonance (CMR) parameters still lacks comprehensive validation. This study aimed to v...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87828-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585877158625280 |
|---|---|
| author | Zhe Zhang Yang Dai Peng Xue Xue Bao Xinbo Bai Shiyang Qiao Yuan Gao Xuemei Guo Yanan Xue Qing Dai Biao Xu Lina Kang |
| author_facet | Zhe Zhang Yang Dai Peng Xue Xue Bao Xinbo Bai Shiyang Qiao Yuan Gao Xuemei Guo Yanan Xue Qing Dai Biao Xu Lina Kang |
| author_sort | Zhe Zhang |
| collection | DOAJ |
| description | Abstract Angio-based microvascular resistance (AMR) as a potential alternative to the index of microcirculatory resistance (IMR) and its relationship with microvascular obstruction (MVO) and other cardiac magnetic resonance (CMR) parameters still lacks comprehensive validation. This study aimed to validate the correlation between AMR and CMR-derived parameters and to construct an interpretable machine learning (ML) model, incorporating AMR and clinical data, to forecast MVO in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PPCI). We enrolled 452 STEMI patients from Nanjing Drum Tower Hospital between 2018 and 2022, who received both PPCI and CMR. After PPCI, AMR measurements and CMR-derived parameters were recorded, and clinical data were gathered. The ML workflow comprised feature selection using the Boruta algorithm, model construction with seven classifiers, hyperparameter optimization via ten-fold cross-validation, model comparison based on the area under the curve (AUC), and a Shapley additive explanations (SHAP) analysis to analyze the significance of different features. 32.29% of patients showed inconsistency between AMR and MVO, but we successfully constructed a predictive model for MVO. Among the classifiers, Extreme gradient boosting (XGBoost) post hyperparameter optimization displayed superior performance, achieving an AUC of 0.911 and 0.846 in the training and validation sets, respectively. SHAP analysis identified AMR as a pivotal predictor of MVO. Although we observed the inconsistency between AMR and MVO but the ML-based construction of MVO prediction model is feasible, which brings the possibility of timely prediction of patients with MVO and timely imposition of interventions during PPCI. |
| format | Article |
| id | doaj-art-d425a077b36446c2b0ffe441ed4b8770 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d425a077b36446c2b0ffe441ed4b87702025-01-26T12:26:25ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-025-87828-5Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning modelZhe Zhang0Yang Dai1Peng Xue2Xue Bao3Xinbo Bai4Shiyang Qiao5Yuan Gao6Xuemei Guo7Yanan Xue8Qing Dai9Biao Xu10Lina Kang11Department of Cardiology, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityDepartment of Cardiology, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityCardiovascular Medical Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical SchoolCardiovascular Medical Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical SchoolDepartment of Cardiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing UniversityDepartment of Cardiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing UniversityCardiovascular Medical Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical SchoolDepartment of Cardiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing UniversityDepartment of Cardiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing UniversityCardiovascular Medical Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical SchoolDepartment of Cardiology, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityDepartment of Cardiology, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical UniversityAbstract Angio-based microvascular resistance (AMR) as a potential alternative to the index of microcirculatory resistance (IMR) and its relationship with microvascular obstruction (MVO) and other cardiac magnetic resonance (CMR) parameters still lacks comprehensive validation. This study aimed to validate the correlation between AMR and CMR-derived parameters and to construct an interpretable machine learning (ML) model, incorporating AMR and clinical data, to forecast MVO in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PPCI). We enrolled 452 STEMI patients from Nanjing Drum Tower Hospital between 2018 and 2022, who received both PPCI and CMR. After PPCI, AMR measurements and CMR-derived parameters were recorded, and clinical data were gathered. The ML workflow comprised feature selection using the Boruta algorithm, model construction with seven classifiers, hyperparameter optimization via ten-fold cross-validation, model comparison based on the area under the curve (AUC), and a Shapley additive explanations (SHAP) analysis to analyze the significance of different features. 32.29% of patients showed inconsistency between AMR and MVO, but we successfully constructed a predictive model for MVO. Among the classifiers, Extreme gradient boosting (XGBoost) post hyperparameter optimization displayed superior performance, achieving an AUC of 0.911 and 0.846 in the training and validation sets, respectively. SHAP analysis identified AMR as a pivotal predictor of MVO. Although we observed the inconsistency between AMR and MVO but the ML-based construction of MVO prediction model is feasible, which brings the possibility of timely prediction of patients with MVO and timely imposition of interventions during PPCI.https://doi.org/10.1038/s41598-025-87828-5ST-segment elevation myocardial infarctionAngio-based microvascular resistanceCardiac magnetic resonanceMicrovascular obstructionMachine learning |
| spellingShingle | Zhe Zhang Yang Dai Peng Xue Xue Bao Xinbo Bai Shiyang Qiao Yuan Gao Xuemei Guo Yanan Xue Qing Dai Biao Xu Lina Kang Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model Scientific Reports ST-segment elevation myocardial infarction Angio-based microvascular resistance Cardiac magnetic resonance Microvascular obstruction Machine learning |
| title | Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model |
| title_full | Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model |
| title_fullStr | Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model |
| title_full_unstemmed | Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model |
| title_short | Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model |
| title_sort | prediction of microvascular obstruction from angio based microvascular resistance and available clinical data in percutaneous coronary intervention an explainable machine learning model |
| topic | ST-segment elevation myocardial infarction Angio-based microvascular resistance Cardiac magnetic resonance Microvascular obstruction Machine learning |
| url | https://doi.org/10.1038/s41598-025-87828-5 |
| work_keys_str_mv | AT zhezhang predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT yangdai predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT pengxue predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT xuebao predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT xinbobai predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT shiyangqiao predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT yuangao predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT xuemeiguo predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT yananxue predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT qingdai predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT biaoxu predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel AT linakang predictionofmicrovascularobstructionfromangiobasedmicrovascularresistanceandavailableclinicaldatainpercutaneouscoronaryinterventionanexplainablemachinelearningmodel |